(a)Massively parallel architecture for computer vision and neural networks We have made a research into a massively parallel computer vision system based on the concept of object-oriented system in this research project. This is because object-oriented systems provide us a good framework for representing parallel processes, as well as for representing complex systems such as computer vision systems. Neural networks, which are quite useful in computer vision, have been also considered from the viewpoint of massive parallelism. The architecture which we have adopted is based on a simple, static dataflow model with a two dimensional mesh communication network, and we have designed a massively parallel machine called AMP. The key issue is to provide a light-weight message handling mechanism, which is inevitable in fine-grained parallel process(i.e.,object system)execution. In addition, we have shown the high efficiency of AMP in computer vision applications by software simulation.(b)Progra
… Moremming language for neural networks We have augmented a programming language Valid, which is a basic functional language for AMP, by introducing the object-oriented concept in order to simplify the description of large-scaled neural networks. The augmented language provides us a mechanism to encapsulate functions of neurons and neuron networks, and also provides us a class library of neural networks.(c)Massively parallel computer vision system based on a neural network We have proposed a massively parallel computer vision system called ICE(Image CEntered)System, which is based on a multi-layered, hierarchical neural network. In this system a result of image understanding is represented in a sequence of combinations of activated units in the highest layer: each of the units corresponds to a word meaning.2.ニューラルネットワークの記述法に関する研究関数型言語Validにオブジェクト指向を導入し,ニューロンを構成する機能関数のカプセル化,ネットワークを構成するニューロン・オブジェクトと機能関数のカプセル化,ニューロン・クラス階層などの知識ライブラリの構築を行ない,複雑なニューラルネットを記述するためのシステムを提案した.3.ニューラルネットをベースとした画像理解モデルの検討多階層の相互結合型ニューラルネットワークを用いた超並列画像理解システムICE(ImageCEntered)Systemの提案・予備実験を行なった.ICEでは,画像理解を「入力画像を複数の属性に分けられた記号的概念に対応付けて,理解の結果を各属性の中で最も強く活性化した記号概念の組で表現すること」ととらえている. Less